- In short
- Sample size for an LLM experiment is calculated from three inputs: the minimum detectable effect, the baseline metric value, and the required confidence level. Because LLM output variance is higher than for deterministic systems, LLM experiments generally need larger samples than traditional software A/B tests to reach significance. An underpowered experiment cannot distinguish a real effect from noise. Statistical significance and practical significance are separate: a significant result can still be too small to justify the cost of shipping and maintaining the change.
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